Despite the impressive performance of current methods has achieved, measuring how reliable the predictions are is also crucial to indicate potential mis-segmented regions or guide user interactions for refinement [1]}, [2]}, [3]}. There are two major types of predictive uncertainties for deep CNNs [4]}: \(aleatoric\)  uncertainty and \(epistemic\)  uncertainty. \(Aleatoric\)  uncertainty depends on noise or randomness in the input testing image, while \(epistemic\)  uncertainty, also known as model uncertainty, can be explained away given enough training data. A lot of works have investigated uncertainty estimation for deep neural networks [4]}, [6]}, [7]} and they mainly focused on high-level image classification or regression tasks. Some recent works [8]}, [9]} investigated test-time dropout-based (epistemic) uncertainty for segmentation.  [10]} and  [9]} extensively investigate different kinds of uncertainties for CNN-based medical image segmentation, including not only epistemic but also aleatoric uncertainties. Even though uncertainty estimation is of great interest in HAN OARs segmentation, to the best of our knowledge, exiting works have not investigated this problem.
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